The use of computer networks has become an integral part of the way businesses provide goods and services to their customers. One advantage the use of the network provides is to enable the distribution of applications and the business logic that they are comprised of closer to the actual user, or customer. This enables these businesses to offer higher levels of service to disparate groups of customers in a wider geographic area than ever before. This opportunity has also enabled to businesses to allow customers access to the network, albeit limited, for example, to directly track their purchases. In this case, each customer may have access to standardized or “tailored” application software packages or to custom developed software packages, to perform desired operations.
As the networks continue to expand in size and utilization, it becomes important that the network be operating properly. For example, timely response to a user request is an important factor in determining whether network components and, consequentially, the network is operating properly. In another aspect, timely completion of a user requested transaction may determine whether the overall system (software and hardware) are operating properly or at least satisfactorily.
However, it is often difficult to determine whether timely responses are monitoring or measuring the execution time and comparing the execution time to a known value, i.e, a threshold. If the execution time is greater that the selected threshold, then the response is considered untimely and an indication is provided to determine the reason for the untimely response. In another aspect, the threshold value may be determined as an average value of a plurality of measured execution times accumulated over a pre-set sample window. In still another aspect, the threshold value may be based on a rolling baseline as an average value of a plurality of measured execution times accumulated over a pre-set time sample window and the window is adjusted by removing the oldest values when adding newer values.
However, convention methods fail to allow for the introduction of network capacity and duress, i.e., load. A static threshold value uses only a single non-changing value which may not bet scalable when a time criterion of network usage is considered. A time based rolling baseline threshold method fails to consider that the time window size is fixed and may not consider additional or reduction in network load. Further this method relies on historical data, is dependent upon the size of the window, and is not performed in real-time. The rolling baseline fails to consider the addition or reduction in load and, although the values within the window change with time, the size of the window affects the threshold value. That is, the smaller the window size the closer the threshold is to the data being measured. But this smaller window comes at the cost of the benefit of processing time and the benefit of averaging the data over a smaller time window.
Hence, there is a need in the industry for a method and apparatus for providing determining network component operation in real-time and adaptive to changes in the network.